Nonlinear Support Vector Machine Model Research Articles

QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine.

The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

An experimental comparison of some efficient approaches for training support vector machines

Support Vector Machines (SVMs) are one of the most widely used techniques for developing classification and regression models. A significant portion of the recent research on SVMs is devoted to the development of efficient computational approaches for SVM training. This paper performs an experimental analysis of some approaches recently developed for training SVM classification models, including decomposition algorithms, explicit solution techniques, and linear programming. The analysis involves the generalizing performance of the SVM models and the computational efficiency of the algorithms. The results lead to useful conclusions on the performance of the training techniques and to the applicability of linear and non-linear SVM models.